Process for rapidly controlling a process variable without overshoot using a time domain polynomial feedback controller

ABSTRACT

A method for controlling a process variable as it approaches a predetermined value (setpoint) so that the setpoint is not exceeded. The method employs a time domain polynomial equation in a feedback configuration and utilizes a controller that acts as an On/Off controller until the process variable approaches setpoint. As the process variable approaches setpoint, the controller acts as a fast responding analog controller thereby “tailoring” a control variable to precisely bring the process variable to the setpoint without exceeding or overshooting the setpoint.

CROSS REFERENCE TO PRIOR APPLICATION

This application is a continuation of, and claims the benefit under 35U.S.C. § 120 of the filing date of, U.S. patent application Ser. No.11/173,024 filed Jul. 1, 2005 which is a continuation in part of, andclaims the benefit under 35 U.S.C. § 120 of the filing date of, U.S.patent application Ser. No. 09/771,799, filed Jan. 29, 2001.

BACKGROUND OF THE INVENTION

This invention relates generally to the field of industrial processcontrol, and particularly to a method for rapidly controlling a measuredvariable of a process from an existing value to a very divergent desiredvalue without an overshoot beyond the new value.

Analog Controllers

An analog controller receives a continuous analog signal input thatrepresents a measured process value or variable (PV) from a sensor andcompares this value to the desired value setpoint (SP) to produce anerror signal (ES). The controller uses this error to calculate anyrequired correction and sends a continuous analog signal output (controlvariable), to a final control element (any continuously variable valve,damper, pump, fan, etc.). The final control element (FCE) then controlsthe process variable.

Proportional-Integral-Derivative (PID)

The original analog controller had only Proportional, or gain, control.This controller compared the process variable to the setpoint and variedthe control variable as a Docket 172.02 lected multiplication value,which could be more or less than one. Because the amount of controlvariable change, due to deviation of the process variable from thesetpoint, decreased as the process variable neared the setpoint, theprocess variable could continue to deviate (droop) from the setpointindefinitely. To overcome this it was necessary to manually offset thesetpoint, above or below, the desired operating value.

A function to overcome this droop was developed and was called Integral,or Reset, and was made a part of the Proportional control. Thisintegrated the error signal as a function of the time during which theoffset continued. The amount of Integral effect was preselected bymanual adjustment.

Because Proportional control and Proportional plus Integral controlcould not react quickly to a process with significant dead time (delaysin a process change in response to a FCE change), another control modewas developed and added to the analog controller. This function wasnamed Derivative and measures the speed of process variable deviationfrom setpoint. The controller calculates an addition or subtraction tothe control variable based on this deviation speed. The magnitude ofderivative action in relation to the speed of deviation is preselectedby manual adjustment.

These three modes of control may be adjusted (tuned) to work well on acontinuous process, but tend to overshoot above and below setpointduring an initial start-up of a process and will oscillate for manycycles. These oscillations may be extreme enough that the processmaterial is ruined or an unsafe situation occurs.

Multiple attempts have been made to control to a predetermined value(setpoint) without the measured parameter (process variable) exceedingthe setpoint using the PID. The original method, and still the mostcommon, to move the process variable to the setpoint is to configure thePID tuning parameters to slow response to process variable disturbance.See FIG. 3. A number of variations to the PID controller have beendeveloped to solve the problem of rapidly moving the process variable,for example, setpoint suppression/reset, ramp/soak, and gap control.Setpoint suppression/reset involves setting an intermediate PID setpointat some value less than the actual setpoint until the process variablereaches that intermediate setpoint. At that point, the controllersetpoint is adjusted to the actual setpoint allowing the processvariable to reach that setpoint. A ramp/soak controller moves the PIDcontroller setpoint in small increments (ramps) over time until thecontroller setpoint reaches the desired setpoint. The controller thenholds the process variable at the desired setpoint (soak). See FIG. 4. Agap controller utilizes a PID controller with a downstream “switch” thatfreezes the final control element when the process variable is within apredetermined band around the setpoint. See FIG. 5.

One of the more recent developments involves using fuzzy logic toanticipate overshoot resulting from the PID calculations. For example,see the patent to Lynch, F. U.S. Pat. No. 5,909,370 (1999) (referred toherein as Lynch). In this method, a fuzzy logic algorithm is used tosuppress the setpoint. This is analogous to the setpointsuppression/reset described above. The fuzzy logic algorithm varies themagnitude of the setpoint suppression.

Currently, over 90% of all analog industrial controllers are a form ofthe PID controller. This controller has been shown to provide theminimum Integrated Average Error (IAE) in continuous controlapplications where process variable overshoot is acceptable; please seeMcMillan, G. (1994) Tuning and Control Loop Performance, InstrumentSociety of America, North Carolina (incorporated herein by reference andreferred to herein as McMillan).

The standard PID is also the primary controller used for applicationswhere overshoot is not allowed. However, PID controllers withno-overshoot tuning parameters result in relatively slow performance.See FIG. 3. The reason for this slow performance is that theno-overshoot tuned PID controller begins adjusting the final controlelement sooner than necessary. Thus, unnecessary time is required tomove the process variable to the setpoint. This is because the quantityof controlled material delivered through the final control element, whenit is not at its full ON position (or OFF position, if applicable to thespecific system), is less than if that control element were fully ON (orOFF) longer.

The most significant shortfall of the traditional PID, when used inapplications where overshoot is not allowed, is that the PID does nothave a feature ensuring the final control element is set OFF (as usedherein, the terms OFF and ON represent succession of the control mediumwhether the process variable approaches the setpoint from above orbelow) as the process variable reaches the setpoint. The PID outputoften does not begin reversing direction (reducing its output after anincreasing output) until after the process variable passes thecontroller's setpoint. Thus, the PID controller does not have systems toprevent or minimize overshoot. Often, a maximum setpoint exists where aprocess operates optimally. In some cases, however, that process cannotexceed that maximum setpoint without damage occurring to the environmentor to the equipment or product. For example, a cereal tastes better whenthe berry is cooked at 99° C. but the berry's sugar is significantlychanged if cooked at 100° C. In the more extreme case of an exothermicreaction, a reactor might explode or the relief devices actuate, if thatmaximum setpoint is exceeded. Without a method to ensure the finalcontrol element is set OFF if the process variable moves beyond thesetpoint, the PID controller cannot ensure this damage does not occur.Thus batches can fail and equipment or environmental damage can occurwhen the PID controller is used for these applications. In theseapplications, control practitioners often set the operating setpointbelow this maximum setpoint. The result is the process does not operateat the optimal point, increasing production times or decreasingproduction yields.

Setpoint suppression/reset, while commonly utilized in applicationswhere overshoot is not allowed, also has slow performance as thecontroller first reduces the final control element's percent ON to meetthe intermediate setpoint. After the intermediate setpoint is reached,the controller increases the final control element's percent ON to reachthe actual setpoint. The controller reduces the percent ON when theprocess variable reaches the actual setpoint. Extra time is required toreach the actual setpoint than if the controller were able to move theprocess variable directly to the setpoint.

Ramp/soak controllers are effective in applications in which overshootis not allowed. Typically, the final control element's percentage ON isin the middle of its percentage ON range. Because the controller'sequipment is not immediately positioned at its desired value, the finalcontrol element is not held at full ON position for the maximum timewhile the process variable is approaching setpoint.

While gap controllers ensure the final control element's output is setOFF when the process variable is near the setpoint, the controller actsas an on/off controller near the setpoint. Because of this action, thecontroller's precision is not the quality of the traditional PID orother controllers.

The fuzzy logic controller proposed by Lynch has the same shortcomingsas the setpoint suppression/reset described above along with the addedcomplexity of the fuzzy logic controller.

Fuzzy logic currently is not supported by most industrial controllersand requires significant computing resources to implement.

Thus, a need exists for a controller that moves the process variable tothe setpoint more rapidly than PID controllers, yet without overshootingthe setpoint.

Feed-Forward Control

Feed-forward control was developed to anticipate control systemcorrections to process disturbances before the actual process receivesthe disturbance. Feed-forward control attempts to measure upsets(disturbances) to the process before the upset reaches the process. Thecontroller then calculates corrections for those upsets. An examplewould be a house having a method to measure whether or not the frontdoor is open and to measure the outside temperature. If the front dooropens and a significant difference exist between the outside and insidetemperatures, the heating/air conditioning system would start althoughthe inside temperature is presently at the desired value.

The most significant shortfall of the feed-forward controller involvesthe requirement that the process under control be well understood. Oftenwhen implementing these controllers, a disturbance (an event that drivesthe process from the setpoint) that was not anticipated by the engineerconfiguring the controller attacks the process. The disturbance can makethe process unstable resulting in process or equipment failure or anunsafe condition.

Model-Based Control

The latest developments in process control have been focused on advancedcontrol algorithms including: state-space variable controllers, neuralnetwork controllers, artificial intelligence controllers, fuzzy logiccontrollers etc, further referred to as model-based controllers.Model-based control uses a mathematical representation of an ideallyoperating process to calculate correction to process upsets. The controlpractitioner develops the model controller based on anticipated orexpected disturbances and the desired process response. Model basedcontrol uses a mathematical representation of the process to calculatecorrections to process upsets. The model is typically developed usingcomplex mathematical systems such as state-space variable matrices.

As the cost of computing systems is falling, using model-basedcontrollers is becoming more practical. For applications where absolutecontrol system accuracy is necessary, model-based controllers offersignificant promise and advancement beyond current technologies.

However, these controllers are very complex and require advancedengineering support to deploy, maintain and modify, increasing the costof the control system. Because of the complexity of this type ofcontroller, significant computing resources are required to implementthese controllers. Most contemporary industrial controllers do not havethese computing resources available and those that do are quiteexpensive. Thus, to date, these controllers have not been widely used inindustrial applications.

Contemporary model-based controllers also require that the process undercontrol be well understood and therefore they suffer from the same shortcoming as feed-forward controllers when the control practitioneroverlooks a source of disturbance. As stated above, this disturbance canmake the process unstable resulting in process or equipment failure oran unsafe condition. Thus, control practitioners hesitate inimplementing these controllers on new processes. In most cases, thecontrol engineer will first install traditional feedback controllers,run the system to ensure all disturbances have been identified and thendesign the model-based controller. Clearly, the cost to installmodel-based controllers on new processes can be prohibitive.

Ingredient Addition/Filling Operations

The main analog technique employed to add ingredients or fill productsis the full/trickle mode where the control element is set to a “minimum”position as the ingredients near the setpoint. The predominant techniqueemployed to add ingredients or fill products is to start adding theproduct at full rate. When the added product nears the setpoint, thecontroller switches to a “trickle” mode in which the product flow isreduced to 10% to 25% of the full rate. When the product quantity iswithin acceptable error tolerances, the product flow is stopped. Thistrickle rate is often implemented with a second final control element.

The full/trickle mode method for ingredient addition has workedsuccessfully for many years. It is especially successful when the motiveforce supplying the ingredients is constant. However, this controller'sprecision is reduced when that motive force varies. This is because thecontroller's full OFF point is dependent upon a fixed quantity ofmaterial being transferred after the final control element is fully OFF.If that force varies, the quantity of material varies. Anothershortcoming of the full/trickle mode for ingredient addition is that, atthe predetermined intermediate setpoint, the final control element isset to a reduced percent ON. A better method would have that finalcontrol element continuously set OFF as the ingredient quantityapproaches the setpoint.

Other Prior Art References:

Traditional control theory texts, such as “Grundlagen derRegelungstechnick” Fundamentals of Control Engineering by Dorrscheidtand Latzel, Teubner-Verlag, Stuttgart, 1993 have referred to usingpolynomials in feedback control strategies.

While control theory texts have suggested that polynomials would makeeffective feedback controllers, these controllers applied polynomials inthe frequency domain, not the time domain. When frequency domainpolynomials of the forms:

$\begin{matrix}{\frac{1}{s^{2} - a^{2}}\mspace{14mu} {or}\mspace{14mu} \frac{s}{s^{2} - a^{2}}} & \;\end{matrix}$

are inverse Laplace transformed back to the time domain, the resultantequations are of the form:

$\frac{1}{a}\sin \; {at}\mspace{14mu} {or}\mspace{14mu} \cos \; {at}$

These equations result in oscillatory response from the controller, anundesirable result for process control where linear outputs arerequired. (Note: t is defined in these examples as the error calculationresult.) If, in a more general form, the polynomial is of higher order:

${{\frac{1}{\left( {s - a} \right)^{n}}\mspace{14mu} {where}\mspace{14mu} n} = 1},2,{3\mspace{14mu} \ldots}$

The inverse Laplace transform is:

$\frac{1}{\left( {n - 1} \right)!}t^{n - 1}^{at}$

References such as Beyer, W. (1981), CRC Standard Mathematical Tables,CRC Press, Inc., Boca Raton, Fla. describe inverse Laplace transforms.

This resultant controller equation includes an exponential function(e^(at)). The exponential function, by definition, does not allow thecontrol variable to go to zero. Thus, if the process variable moves pastthe setpoint, the controller's output would still be greater than zerocontinuing to add energy or ingredients erroneously.

In order for a controller to be successfully employed in contemporaryindustrial process controllers (simple software based computing type,traditional electrical/electronic, pneumatic, hydraulic, etc.), thecontroller's equations need to be time domain based. Though frequencydomain type controllers have been used in linear control applications(aircraft control for example) for some time, these controllers requiresignificant computing resources or electronics to deploy. The frequencydomain controller is not economically feasible to be utilized forprocess control applications.

Previous attempts have been made that use an asymptotic approach tosetpoint, specifically Rae, Richard, U.S. Pat. No. 4,948,950(incorporated herein by reference and referred to herein as Rae).However, Rae's method uses a linear algebraic equation for developmentof the “. . . the target slope below the setpoint temperature or is thetarget rate of change of the temperature of the output heating effect ofthe heating means . . . .” Because the equation is a linear function,the process variable does not approach setpoint as quickly as if theequation incorporated an ^(n)th-order exponential term. Thus, thecontrol equation proposed by Rae wastes resources, for example time andenergy, when applied to a system in which movement of the processvariable to setpoint as rapidly as possible, without overshoot, is thekey control method selection criteria.

BRIEF SUMMARY OF THE INVENTION

This invention is a method for rapidly controlling a process variable toa setpoint without overshoot using a time domain polynomial feedbackcontroller. This controller first sets its output to zero if the erroris negative. Otherwise the controller's output is calculated from theerror signal using a time domain polynomial equation. For applicationswhere long-term setpoint maintenance is required, the controllerincludes a method to automatically execute an integral correctionfeature. After the process variable has reached setpoint, the controlleralso includes a feature to automatically improve the controller'sparameters. The resulting method controls process variable on its pathto the desired setpoint in a quicker manner than contemporary industrialcontrollers.

This controller may also be utilized in product filling/ingredientaddition applications resulting in lower material usage due to the moreprecise control.

Objects and Advantages

Accordingly, several objects and advantages of this invention are:

-   -   It provides a method to control a process variable to a setpoint        without overshoot more rapidly than traditional        Proportional-Integral-Derivative (PID) controller tuned for no        overshoot.    -   It is easier to understand than the PID and other controllers.    -   It requires fewer resources (hardware, software, etc.) than        other controllers and, because it is time domain based, may be        directly implemented in contemporary industrial controllers.    -   It ensures the controller output is zero if the process variable        moves beyond the setpoint while maintaining full control of the        process variable until the process variable reaches the setpoint        in a feedback controller configuration.    -   It reduces required energy to reach setpoint in process control        applications where overshoot is not allowed.    -   It reduces raw material/product variability in ingredient        addition/filling applications employing an analog final control        element.    -   It may be implemented with traditional sensors and final control        elements.

Further objects and advantages of this invention will become apparentfrom consideration of the drawings and ensuing description.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram of a control system including the polynomialcontroller as utilized in accordance with the method of the presentinvention.

FIG. 2 is a flow chart of operations that comprise the method of thepresent invention.

FIG. 3 is a prior art plot illustrating a process variable controlled bya properly tuned PID controller and a PID controller tuned for noovershoot.

FIG. 4 is a prior art plot illustrating an ideal process variablecontrolled by a properly tuned Ramp-Soak PID controller.

FIG. 5 is a prior art plot illustrating a process variable controlled bya Gap PID controller.

FIG. 6 is a plot illustrating a process variable controlled by a PIDcontroller tuned for no overshoot and a process variable controlled by atime domain feedback polynomial controller with typical parameters.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of a standard process/system in need ofcontrol with a feedback control system. This system has: a generalprocess or system 11, a sensor 12 to measure a process variable 13, ananalog controller 14 and a final control element 17 as its generalequipment.

The process variable is a parameter that is an indication of thechemical or physical state of that system. The controller is a hardwareor software based device that is used to calculate corrections todifferences between a setpoint and the measurement. The first operationwithin the controller is a means to calculate an error signal 15 that isthe difference between a setpoint and the process variable. Thecontroller operates upon the error signal 15 to calculate a controlvariable 16. The control variable is the position at which the finalcontrol element needs to operate in order for the process variable toreach and maintain the setpoint. The final control element may be anyvariable output device. While a valve is shown in FIG. 1, any variableoutput device may be used.

An embodiment of the controller of this invention is illustrated in FIG.2: Asymptotic Approach Algorithm Flowchart. The controller has a means20 to calculate an error signal. The controller compares the errorsignal 15 to zero. If the error signal 15 is negative, the controller'soutput 16 is set to zero. If the error signal 15 is positive, the output16 is calculated using an inverted polynomial equation having the formY=A(x)^(P)+B(x)−C or the form Y=A(x)^(P)−B such as a time-domainpolynomial Equation 28 of the type as follows:

Output_(c) =K _(a)(Error)^(P) −K _(Bias)

Where:

K_(a) is Term 1 Gain (unitless)

^(P) is Exponential Term (unitless)

K_(Bias) is Output Bias (unitless)

Returning to FIG. 2, the control variable 16 is checked to ensure thatit is less than 100% 30. If it is not, the control variable 16 is set to100% 34. The control variable is checked to ensure that it is positive32 if it is less than 100%. If negative, the control variable 16 is setto zero 24.

The controller checks to determine if Integral Correction 36 is to beexecuted. If Integral Correction 36 is selected, the controller checksto determine if the process variable 13 is within a user selected errorband E_(i) for a user selected time E_(t) 38. If Integral Correction isactivated 38, a means to integrate and average the error signal 15 overtime is used. The result of the averaged-integrated error signal isadded, positively or negatively, to the polynomial equation output asfollows:

Output_(c) =K _(a)(Error)^(P) −K _(Bias)+Integrated_Error

While not the only method, one method to integrate the error is shown inFIG. 2. At user defined intervals 40, the current error signal 42 (and15), is “Pushed” or loaded into the first position of a Z elementsoftware stack 44. At this same time, the Z^(th) element is “Popped” orunloaded from the stack and discarded 46. The stack is summed andaveraged as described above 48. If integral correction is active 50 andthe error is negative 22, set each element of the previously definedsoftware stack to zero 51.

If the error signal 15 is negative while Integral Correction 38 isactive, averaged integrated error output is set to zero 52. Thus theintegration operation will start from zero the next time IntegralCorrection 38 is executed. The next function of the controller is a userselectable method to improve the K_(Bias) term. If the user has selectedAutomatic Bias Improvement 52, the error signal 15 is checked against auser selected K_(bias) _(—) _(adj) 54 at the time point 38 that IntegralCorrection is initiated if used. If the error signal is greater thanK_(bias) _(—) _(adj) 54 and positive, the new K_(bias) _(—) _(adj) iscalculated as follows 60:

K _(Bias) =K _(Bias)−(Error/2).

If the error signal is greater than K_(bias) _(—) _(adj) 54 and negative56, the new K_(bias) _(—) _(adj) is calculated as follows 58:

K _(Bias) =K _(Bias) +ABS(Error)+1 [where ABS is absolute valuefunction]

If the process remains in need of control, the algorithm is repeatedfrom the top of the flowchart.

Pseudocode For Asymptotic Approach Algorithm

The following is a pseudocode description of this invention:

10 If Process Variable > Setpoint (for reverse acting process) {IfProcess Variable<Setpoint (for direct acting processes)} [IF-THEN-ELSEStructure 1] 20 Then: 30 Calculate the error signal: Error = Measurement− Setpoint [for reverse acting processes] Error = Setpoint − Measurement[for direct acting processes.] 40 Calculate Control Variable: Output_(c)= K_(a)(Error)^(P) − K_(Bias) Where: K_(a) is Term 1 Gain (unitless)^(P) is Exponential Term (unitless) K_(Bias) is Output Bias (unitless)Output_(c) is Equation Output 50 If Output_(c) > 100% [IF-THEN-ELSEStructure 2] 60 Then 70 Output = 100% [maximizing controller input toprocess] 80 Else [IF-THEN-ELSE Structure 2] 90 If Output_(c) < 0%[IF-THEN-ELSE Structure 3] 100 Then 110 Set Output to 0% [stoppingcontroller input to process] 120 Else [IF-THEN-ELSE Structure 3] 130Output = Output_(c) 140 If Error < E_(i) for E_(t) [IF-THEN-ELSEStructure 4] [Where: E_(i) = User Selected Error at which pointpolynominal calculation stops execution and integral correction beginsexecution. If user does not desire Integral Correction, this value isset to zero. E_(t) = User Selected Time at which point polynominalcalculation stops execution and integral correction begins execution.]150 Then: 160 If Time < T_(i) [IF-THEN-ELSE Structure 5] [Where: T_(i) =User Selected Integral Time Period] 170 Then 180 Integral = K_(i)[Where: T_(i) = User Selected Integral Time Period 190 Push Integral toZ element Integral Stack 200 Pop Z^(th) element from Integral Stack] 210Else [IF-THEN-ELSE Structure 5] 220 Endif [IF-THEN-ELSE Structure 5] 230${Output} = {{Output}_{c} + {\sum\limits_{Z = 1}^{Z = n}\; {{Stack}_{n}\mspace{14mu} l\mspace{14mu} Z}}}$240 If User Selected Automatic Parameter Improvement [IF-THEN-ELSEStructure 6] 250 Then 260 If Error > K_(bias) _(—) _(adj) [IF-THEN-ELSEStructure 7] 270 Then 280 K_(Bias) = K_(Bias) − (Error/2) 290 Else[IF-THEN-ELSE Structure 7] 300 If Error < 0 [IF-THEN-ELSE Structure 8]310 Then 320 K_(Bias) = K_(Bias) + ABS(Error) + 1 [where ABS is absolutevalue function] 330 Else [IF-THEN-ELSE Structure 8] 340 Endif[IF-THEN-ELSE Structure 8] 350 Else [IF-THEN-ELSE Structure 7] 360 Endif[IF-THEN-ELSE Structure 7] 370 Endif [IF-THEN-ELSE Structure 6] 380 Else[IF-THEN-ELSE Structure 4] 390 Endif [IF-THEN-ELSE Structure 4] 400Endif [IF-THEN-ELSE Structure 3] 410 Endif [IF-THEN-ELSE Structure 2]420 Else: [IF-THEN-ELSE Structure 1] 430 Set Output to 0% [stoppingcontroller input to process] 440 If Integral Cerrection is active[IF-THEN-ELSE Structure 9] 450 Set each element of PI stack to Zero 460Else [IF-THEN-ELSE Structure 9] 470 Endif [IF-THEN-ELSE Structure 9] 480Endif [IF-THEN-ELSE Structure 1]Repeat algorithm while process is in operation

Additional Embodiment

A means is provided for inclusion of a first order K_(b) term in thecontroller Equation 28:

Output_(c) =K _(a)(Error)^(P) +K _(b) −K _(Bias)

Where:

K_(a) is Term 1 Gain (unitless)

^(P) is Exponential Term (unitless)

K_(b) is Term 2 Gain (unitless)

K_(Bias) is Output Bias (unitless)

It is understood that a K_(b) term will be set to zero in almost allapplications. This is because a similar curve may be obtained from thisequation with a K_(b) greater than zero as the curve generated withEquation 28 with a P term between 1.0 and 2.0. However, the K_(b) isincluded here for completeness of the controller and to allow the useranother method to achieve a specific process variable curve.

Operation—FIGS. 1 and 2

The controller first calculates the error signal 15. The error signal 15is then checked to verify the error signal 15 is positive. If not, thecontrol variable 16 is set to zero along with the Integral Correction38, if used.

If the error signal 15 is positive, the controller uses the error signal15 to calculate the final control element's 17 position. The errorsignal 15 is raised to the power ^(P). Thus, the process variableapproaches the setpoint asymptotically or follows a parabolic curve whenapproaching the setpoint. See FIG. 6 100. IF process and instrumentsystems were ideal, the energy or ingredients would not continue to besupplied to the system at the point the process variable 13 equals thesetpoint as the final control element is set to zero at this point.However, systems take time to react. Final control elements need time toposition, processes need time to react or operate, and sensors need timeto measure. The summation of this time quantity is known as dead-time.To overcome the problem of dead-time in this invention, a userconfigured K_(Bias) is subtracted from the intermediate controlvariable. This ensures the final control element 17 is set to zero whilethe dead-time expires and the process variable 13 does not exceed thesetpoint.

If the user selectable Integral Correction 36 is selected, it acts toovercome differences between the setpoint and the process variable 13.The user would select Integral Correction 36 if long-term setpointmaintenance were desired. An example application would include batchreactor temperature adjustment application where the reactor'singredient temperature moves toward ambient temperature over time.Integral Correction 36 would be bypassed for applications where thesystem is reset immediately after the control variable is set to zero.Example applications where Integral Correction 36 would not be usedwould be ingredient addition or product filling type applications.

After the controller moves the process variable 13 close to thesetpoint, the system may switch to Integral Correction if configured todo so by the user. If selected, Integral Correction 38 integrates theerror of recent time and adds that integrated-averaged resultant to thecontrol variable. Thus any difference between the process variable andthe setpoint will be eliminated. To minimize process variable 13overshoot after the controller switches to Integral Correction 38, theaveraged-integrated error resultant is set to zero 52 along with anyintegration “history” if the process variable 13 moves beyond thesetpoint while Integral Correction 38 is active. Thus, the integrationoperation will start from zero the next time Integral Correction 38 isexecuted.

To improve the K_(bias) term, the controller includes a user selectableoption to automatically adjust that term. At the same time point thatIntegral Correction 38 would be executed, Automatic Bias Improvement isexecuted, if the user has so selected. The error signal 15 is firstchecked to ensure it is positive. If positive, the current error isdivided by two and that becomes the new K_(bias). If negative, one isadded to the current K_(bias). The algorithm is repeated as long as theprocess/system 11 is active.

Thus, as seen in FIG. 6, this control method moves the process variable13 to the setpoint more rapidly than does the PID controller 102 tunedfor no overshoot. Because of this, applications in which overshoot isnot allowed will use less energy or ingredients to reach setpoint whenthis controller is used. In practice, notable reductions in processexecution times, and energy used, have been realized.

If the process variable moves beyond the setpoint, this controllerensures the output is set OFF due to FIG. 2, 22 along with K_(Bias)task. Thus, overshoot is prevented while having the process variablerapidly move to the setpoint when this controller is properly applied.

Also, the controller utilizes a relatively simple polynomial equation toderive its control variable and does not require significant hardware orcomputing resources to deploy. Another advantage of the polynomialequation based controller is it is easier to understand than calculusbased functions of the controller types.

As the controller does not require significant resources to deploy, itmay easily be implemented using contemporary industrial controllers,sensors, and final control elements.

As the method that the process variable approaches the setpoint is asmooth continuously decreasing asymptote, this controller offers animproved method to add ingredients or fill products over thefull/trickle method. This is because the full/trickle method had threediscrete step reductions in ingredient flow: full, trickle, and off.Thus, product variability is reduced as the control precision isincreased.

It is to be understood that while certain forms of this invention havebeen illustrated and described, it is not limited thereto except insofaras such limitations are included in the following claims and allowableequivalents thereof.

1. A method for controlling the transfer of a material into a containerwithout overshooting a set point indicative of the container beingfilled to a desired amount using a feedback controller which generates acontroller output signal which is communicated to a variable outputdevice by which the flow of material into the container may be varied inaccordance with the controller output signal; the method comprising thesteps of: measuring a process variable indicative of the amount ofmaterial added to the container; calculating an error signal bycomparing the process variable to the set point; if the error signal isless than or equal to zero, setting the controller output signal to zeroto prevent flow of material through the variable output device and intothe container; if the error signal is positive, calculating thecontroller output signal using a polynomial equation, which includes theerror signal raised to an exponential term reduced by a bias tuningparameter, and if the value of the controller output signal iscalculated as equal to or less than zero, setting the controller outputsignal to zero; communicating the controller output signal to thevariable output device to control the flow of material therethrough andinto the container without overshooting the set point; and repeating thesteps until the controller output signal communicated to the variableoutput device is zero.
 2. The method as in claim 1 wherein thecontroller output signal is calculated using the equationOutput_(c)=K_(a)(Error)^(P)−K_(Bias); wherein Output_(c) is controlleroutput signal, K_(a) represents gain, Error is the error signal, P isthe exponential term to which the error signal is raised, and K_(Bias)represents the bias tuning parameter.
 3. The method of claim 2comprising the further step of selectively adjusting the bias tuningparameter, after the controller output signal is set to zero, bycomparing the error signal to a bias adjustment parameter and if theerror signal is greater than the bias adjustment parameter and positive,the bias tuning parameter is recalculated by subtracting a selectedamount from the bias tuning parameter.
 4. The method of claim 3 whereinin the step of recalculating the bias tuning parameter, the amountsubtracted from said bias tuning parameter is equal to half of the errorsignal.
 5. The method of claim 3 wherein if the error signal is greaterthan the bias adjustment parameter and negative, then the bias tuningparameter is recalculated by adding to the bias tuning parameter anamount equal to one plus the absolute value of the error signal.
 6. Amethod for controlling the transfer of material into a succession ofcontainers without overshooting a set point indicative of each containerbeing filled to a desired amount using a feedback controller whichgenerates a controller output signal which is communicated to a variableoutput device by which the flow of material into each container may bevaried in accordance with the controller output signal; the methodcomprising the steps of: a) measuring a process variable indicative ofthe amount of material added to a first of the containers; b)calculating an error signal by comparing the process variable versus theset point; c) if the error signal is less than or equal to zero, settingthe controller output signal to zero to prevent flow of material throughthe variable output device and into the container; d) if the errorsignal is positive, calculating the controller output signal using apolynomial equation, which includes the error signal raised to anexponential term reduced by a bias tuning parameter and if the value ofthe controller output signal is calculated as equal to or less thanzero, setting the controller output signal to zero; e) communicating thecontroller output signal to the variable output device to control theflow of material therethrough and into the first container withoutovershooting the set point; f) repeating steps a) through e) until thecontroller output signal communicated to the variable output device iszero; and then g) repeating steps a) through f) for each of saidsuccession of containers after said first container.
 7. The method ofclaim 6 wherein the controller output signal is calculated using theequation Output_(c)=K_(a)(Error)^(P)−K_(Bias); wherein Output_(c) iscontroller output signal, K_(a) represents gain, Error is the errorsignal, P is the exponential term to which the error signal is raised,and K_(Bias) represents the bias tuning parameter.
 8. The method ofclaim 7 comprising the further step of selectively adjusting the biastuning parameter, after the controller output signal is set to zero, bycomparing the error signal to a bias adjustment parameter and if theerror signal is greater than the bias adjustment parameter and positive,the bias tuning parameter is recalculated by subtracting a selectedamount from the bias tuning parameter.
 9. The method of claim 8 whereinin the step of recalculating the bias tuning parameter, the amountsubtracted from said bias tuning parameter is equal to half of the errorsignal.
 10. The method of claim 8 wherein if the error signal is greaterthan the bias adjustment parameter and negative, then the bias tuningparameter is recalculated by adding to the bias tuning parameter anamount equal to one plus the absolute value of the error signal.